"""
COMPREHENSIVE TEST CASE STUDY
==============================
Scenario: AI Safety Discussion between researchers
Purpose: Demonstrate full realization crystallization pipeline

Test Objectives:
1. Extract realizations from complex multi-turn conversation
2. Calculate Q-scores with full transparency
3. Assign layers automatically
4. Build parent-child graph (بنات افكار)
5. Test retrieval system
6. Assess system performance
"""

from layers.layer_2_core.realization_engine import RealizationEngine, RealizationFeatures
import json
from datetime import datetime


class TestCaseStudy:
    def __init__(self):
        self.engine = RealizationEngine()
        self.test_results = {
            'test_name': 'AI Safety Discussion Analysis',
            'timestamp': datetime.now().isoformat(),
            'realizations': [],
            'metrics': {},
            'assessment': {}
        }
    
    def run_test(self):
        """Execute complete test pipeline"""
        print("="*80)
        print("TEST CASE STUDY: AI SAFETY DISCUSSION")
        print("="*80)
        print("\n🔄 PHASE 1: CONVERSATION SIMULATION\n")
        
        # Simulate a multi-turn conversation about AI safety
        self.simulate_conversation()
        
        print("\n🔄 PHASE 2: REALIZATION EXTRACTION & SCORING\n")
        self.extract_and_score_realizations()
        
        print("\n🔄 PHASE 3: LAYER DISTRIBUTION ANALYSIS\n")
        self.analyze_layer_distribution()
        
        print("\n🔄 PHASE 4: GENERATIVITY ANALYSIS (بنات افكار)\n")
        self.analyze_generativity()
        
        print("\n🔄 PHASE 5: RETRIEVAL SYSTEM TEST\n")
        self.test_retrieval()
        
        print("\n🔄 PHASE 6: QUALITY ASSESSMENT\n")
        self.assess_quality()
        
        print("\n🔄 PHASE 7: EXPORT RESULTS\n")
        self.export_results()
        
        print("\n" + "="*80)
        print("TEST COMPLETE")
        print("="*80)
    
    def simulate_conversation(self):
        """Simulate AI safety discussion"""
        print("Simulating 8-turn conversation between AI safety researchers...")
        print("Topic: Alignment, interpretability, and emergent behaviors\n")
    
    def extract_and_score_realizations(self):
        """Extract realizations from conversation and score them"""
        
        # ===================================================================
        # TURN 1: Foundational observation about AI systems
        # ===================================================================
        print("Turn 1: Discussing AI scaling...")
        r1 = self.engine.add_realization(
            content="Larger language models exhibit emergent capabilities not present in smaller models",
            features=RealizationFeatures(
                grounding=0.95,      # Well-documented (GPT-3, GPT-4 papers)
                certainty=0.92,      # Strong empirical evidence
                structure=0.90,      # Clear statement
                applicability=0.88,  # Applies to model development
                coherence=1.0,       # No contradictions
                generativity=0.85    # Generates safety questions
            ),
            turn_number=1,
            context="Researcher A observes scaling trends",
            evidence=["GPT-3 paper", "Emergent Abilities paper"]
        )
        self.test_results['realizations'].append({
            'id': r1.id,
            'content': r1.content,
            'q_score': r1.q_score,
            'layer': r1.layer
        })
        
        # ===================================================================
        # TURN 2: The alignment problem emerges
        # ===================================================================
        print("Turn 2: Identifying the core problem...")
        r2 = self.engine.add_realization(
            content="AI systems optimize for specified objectives, not intended outcomes - this is the alignment problem",
            features=RealizationFeatures(
                grounding=0.92,      # Well-established in AI safety literature
                certainty=0.95,      # Core problem, high certainty
                structure=0.93,      # Clear problem statement
                applicability=0.94,  # Critical for AI development
                coherence=0.95,      # Consistent with Turn 1
                generativity=0.90    # Generates research directions
            ),
            turn_number=2,
            parents=[r1.id],
            context="Researcher B identifies misalignment risk",
            evidence=["Superintelligence by Bostrom", "AI Alignment Forum"]
        )
        self.test_results['realizations'].append({
            'id': r2.id,
            'content': r2.content,
            'q_score': r2.q_score,
            'layer': r2.layer
        })
        
        # ===================================================================
        # TURN 3: Interpretability as solution direction
        # ===================================================================
        print("Turn 3: Proposing interpretability approach...")
        r3 = self.engine.add_realization(
            content="Mechanistic interpretability - understanding model internals - is necessary for alignment",
            features=RealizationFeatures(
                grounding=0.85,      # Emerging field, less established
                certainty=0.80,      # Strong belief but not proven
                structure=0.88,      # Clear proposal
                applicability=0.90,  # Highly actionable
                coherence=0.92,      # Follows from alignment problem
                generativity=0.88    # Generates research methods
            ),
            turn_number=3,
            parents=[r2.id],
            context="Researcher C proposes interpretability research",
            evidence=["Anthropic's interpretability work", "Circuits papers"]
        )
        self.test_results['realizations'].append({
            'id': r3.id,
            'content': r3.content,
            'q_score': r3.q_score,
            'layer': r3.layer
        })
        
        # ===================================================================
        # TURN 4: Measurement challenge
        # ===================================================================
        print("Turn 4: Identifying measurement problem...")
        r4 = self.engine.add_realization(
            content="We cannot fully verify AI system behavior - the testing problem is computationally intractable",
            features=RealizationFeatures(
                grounding=0.98,      # Computational complexity theory
                certainty=0.90,      # Strong theoretical backing
                structure=0.92,      # Clear limitation
                applicability=0.85,  # Constrains verification approaches
                coherence=0.88,      # Complicates Turn 3
                generativity=0.82    # Generates verification methods
            ),
            turn_number=4,
            parents=[r3.id],
            context="Researcher D identifies fundamental limit",
            evidence=["Computational complexity", "Verification literature"]
        )
        self.test_results['realizations'].append({
            'id': r4.id,
            'content': r4.content,
            'q_score': r4.q_score,
            'layer': r4.layer
        })
        
        # ===================================================================
        # TURN 5: Sandbox approach
        # ===================================================================
        print("Turn 5: Proposing containment strategy...")
        r5 = self.engine.add_realization(
            content="AI systems should be developed in sandboxed environments with capability constraints",
            features=RealizationFeatures(
                grounding=0.80,      # Practical approach, less theoretical
                certainty=0.75,      # Uncertain effectiveness
                structure=0.85,      # Clear strategy
                applicability=0.92,  # Very actionable
                coherence=0.85,      # Partial solution to Turn 4
                generativity=0.78    # Generates safety protocols
            ),
            turn_number=5,
            parents=[r4.id],
            context="Researcher E proposes containment",
            evidence=["Capability control literature"]
        )
        self.test_results['realizations'].append({
            'id': r5.id,
            'content': r5.content,
            'q_score': r5.q_score,
            'layer': r5.layer
        })
        
        # ===================================================================
        # TURN 6: Multi-agent coordination insight
        # ===================================================================
        print("Turn 6: Discovering coordination dynamics...")
        r6 = self.engine.add_realization(
            content="Multiple AI systems will exhibit emergent coordination behaviors not predictable from individual analysis",
            features=RealizationFeatures(
                grounding=0.82,      # Game theory + emergence literature
                certainty=0.85,      # Strong theoretical basis
                structure=0.88,      # Clear prediction
                applicability=0.80,  # Applies to multi-agent systems
                coherence=0.90,      # Extends Turn 1 (emergence)
                generativity=0.92    # Opens multi-agent research
            ),
            turn_number=6,
            parents=[r1.id, r2.id],
            context="Researcher F identifies multi-agent risk",
            evidence=["Multi-agent RL", "Game theory"]
        )
        self.test_results['realizations'].append({
            'id': r6.id,
            'content': r6.content,
            'q_score': r6.q_score,
            'layer': r6.layer
        })
        
        # ===================================================================
        # TURN 7: Synthesis - layered safety
        # ===================================================================
        print("Turn 7: Synthesizing into framework...")
        r7 = self.engine.add_realization(
            content="AI safety requires layered defenses: interpretability + verification + containment + coordination protocols",
            features=RealizationFeatures(
                grounding=0.88,      # Synthesizes prior work
                certainty=0.87,      # Confident in framework
                structure=0.92,      # Clear framework
                applicability=0.95,  # Highly actionable
                coherence=0.95,      # Synthesizes Turns 3-6
                generativity=0.88    # Generates integrated approach
            ),
            turn_number=7,
            parents=[r3.id, r4.id, r5.id, r6.id],
            context="Researcher A synthesizes discussion",
            evidence=["Defense in depth", "Security engineering"]
        )
        self.test_results['realizations'].append({
            'id': r7.id,
            'content': r7.content,
            'q_score': r7.q_score,
            'layer': r7.layer
        })
        
        # ===================================================================
        # TURN 8: Meta-realization
        # ===================================================================
        print("Turn 8: Meta-observation about the discussion...")
        r8 = self.engine.add_realization(
            content="This conversation itself demonstrates how realizations build on each other to form coherent frameworks",
            features=RealizationFeatures(
                grounding=0.90,      # Observable in this conversation
                certainty=0.88,      # We can see it happening
                structure=0.94,      # Very clear observation
                applicability=0.85,  # Applies to knowledge work
                coherence=0.98,      # Meta-coherent
                generativity=0.90    # Self-referential insight
            ),
            turn_number=8,
            parents=[r7.id],
            context="Researcher B observes the process",
            evidence=["This very conversation"]
        )
        self.test_results['realizations'].append({
            'id': r8.id,
            'content': r8.content,
            'q_score': r8.q_score,
            'layer': r8.layer
        })
        
        print(f"\n✅ Extracted {len(self.test_results['realizations'])} realizations")
    
    def analyze_layer_distribution(self):
        """Analyze how realizations distributed across layers"""
        
        # Get statistics
        self.engine.print_stats()
        
        # Store in results
        self.test_results['metrics']['layer_distribution'] = dict(
            self.engine.stats['layer_distribution']
        )
        self.test_results['metrics']['avg_q_score'] = self.engine.stats['avg_q_score']
        
        # Analyze quality by layer
        print("\nQuality Analysis by Layer:")
        for layer in [0, 1, 2, 3, 'N']:
            realizations = list(self.engine.layers[layer].values())
            if realizations:
                avg_q = sum(r.q_score for r in realizations) / len(realizations)
                min_q = min(r.q_score for r in realizations)
                max_q = max(r.q_score for r in realizations)
                print(f"  Layer {layer}: avg={avg_q:.4f}, min={min_q:.4f}, max={max_q:.4f}")
    
    def analyze_generativity(self):
        """Analyze which realizations were most generative"""
        
        print("Most Generative Realizations (بنات افكار):\n")
        
        # Find realizations with children
        with_children = [
            (r, len(r.children)) 
            for r in self.engine.index.values() 
            if r.children
        ]
        with_children.sort(key=lambda x: x[1], reverse=True)
        
        generativity_data = []
        
        for i, (r, child_count) in enumerate(with_children[:5], 1):
            print(f"{i}. {r.content[:60]}...")
            print(f"   Q={r.q_score:.4f}, Layer {r.layer}")
            print(f"   Generated {child_count} children:")
            
            children_info = []
            for child_id in r.children:
                child = self.engine.index[child_id]
                print(f"     → {child.content[:50]}... (Q={child.q_score:.3f})")
                children_info.append({
                    'content': child.content,
                    'q_score': child.q_score
                })
            
            generativity_data.append({
                'parent': r.content,
                'q_score': r.q_score,
                'child_count': child_count,
                'children': children_info
            })
            print()
        
        self.test_results['metrics']['generativity'] = generativity_data
    
    def test_retrieval(self):
        """Test the retrieval system"""
        
        queries = [
            ("alignment", "alignment problem"),
            ("interpretability", "understanding models"),
            ("verification", "testing problem"),
            ("safety framework", "layered defenses"),
            ("emergence", "emergent capabilities")
        ]
        
        retrieval_results = []
        
        for query_name, query in queries:
            print(f"Query: '{query}'")
            results = self.engine.retrieve(query)
            
            if results:
                best = results[0]
                print(f"  ✅ Found: [{best.layer}] Q={best.q_score:.4f}")
                print(f"     {best.content[:60]}...")
                
                retrieval_results.append({
                    'query': query,
                    'found': True,
                    'best_match': {
                        'content': best.content,
                        'q_score': best.q_score,
                        'layer': best.layer
                    }
                })
            else:
                print(f"  ❌ No results")
                retrieval_results.append({
                    'query': query,
                    'found': False
                })
            print()
        
        self.test_results['metrics']['retrieval'] = retrieval_results
        
        # Calculate retrieval accuracy
        found_count = sum(1 for r in retrieval_results if r['found'])
        accuracy = found_count / len(queries) * 100
        print(f"Retrieval Accuracy: {found_count}/{len(queries)} = {accuracy:.1f}%")
    
    def assess_quality(self):
        """Comprehensive quality assessment"""
        
        print("="*60)
        print("QUALITY ASSESSMENT")
        print("="*60)
        
        # 1. Q-Score Distribution
        q_scores = [r.q_score for r in self.engine.index.values()]
        q_scores.sort(reverse=True)
        
        print("\n1. Q-Score Distribution:")
        print(f"   Highest: {max(q_scores):.4f}")
        print(f"   Lowest:  {min(q_scores):.4f}")
        print(f"   Mean:    {sum(q_scores)/len(q_scores):.4f}")
        print(f"   Median:  {q_scores[len(q_scores)//2]:.4f}")
        
        # 2. Layer Quality
        print("\n2. Layer Quality:")
        print(f"   Layer 0 (Universal): {self.engine.stats['layer_distribution'][0]} realizations")
        print(f"   Layer 1 (Domain):    {self.engine.stats['layer_distribution'][1]} realizations")
        print(f"   Layer 2 (Pattern):   {self.engine.stats['layer_distribution'][2]} realizations")
        print(f"   Layer 3 (Situation): {self.engine.stats['layer_distribution'][3]} realizations")
        print(f"   Layer N (Ephemeral): {self.engine.stats['layer_distribution']['N']} realizations")
        
        # 3. Coherence Analysis
        print("\n3. Coherence Analysis:")
        avg_coherence = sum(r.features.coherence for r in self.engine.index.values()) / len(self.engine.index)
        print(f"   Average Coherence: {avg_coherence:.4f}")
        print(f"   → {avg_coherence*100:.1f}% consistency with prior layers")
        
        # 4. Generativity Analysis
        print("\n4. Generativity Analysis:")
        total_children = sum(len(r.children) for r in self.engine.index.values())
        total_parents = sum(len(r.parents) for r in self.engine.index.values())
        avg_children = total_children / len(self.engine.index)
        print(f"   Total children spawned: {total_children}")
        print(f"   Average children per realization: {avg_children:.2f}")
        print(f"   Total parent links: {total_parents}")
        
        # 5. Feature Analysis
        print("\n5. Feature Averages:")
        features = ['grounding', 'certainty', 'structure', 'applicability', 'coherence', 'generativity']
        for feature in features:
            avg = sum(getattr(r.features, feature) for r in self.engine.index.values()) / len(self.engine.index)
            print(f"   {feature.capitalize():15s}: {avg:.4f}")
        
        # 6. System Performance
        print("\n6. System Performance:")
        print(f"   Total realizations: {len(self.engine.index)}")
        print(f"   Layers used: {sum(1 for v in self.engine.stats['layer_distribution'].values() if v > 0)}/5")
        print(f"   Graph depth: {self.calculate_max_depth()} levels")
        print(f"   Avg Q-score: {self.engine.stats['avg_q_score']:.4f}")
        
        # Store assessment
        self.test_results['assessment'] = {
            'q_score_stats': {
                'max': max(q_scores),
                'min': min(q_scores),
                'mean': sum(q_scores)/len(q_scores),
                'median': q_scores[len(q_scores)//2]
            },
            'coherence': {
                'avg_coherence': avg_coherence,
                'consistency_pct': avg_coherence * 100
            },
            'generativity': {
                'total_children': total_children,
                'avg_children_per_realization': avg_children,
                'total_parent_links': total_parents
            },
            'system_performance': {
                'total_realizations': len(self.engine.index),
                'layers_used': sum(1 for v in self.engine.stats['layer_distribution'].values() if v > 0),
                'graph_depth': self.calculate_max_depth(),
                'avg_q_score': self.engine.stats['avg_q_score']
            }
        }
        
        # 7. Pass/Fail Criteria
        print("\n7. Test Criteria:")
        tests = [
            ("All Q-scores >= 0.70", all(q >= 0.70 for q in q_scores)),
            ("Average Q-score >= 0.85", self.engine.stats['avg_q_score'] >= 0.85),
            ("At least 1 Layer 1+ realization", self.engine.stats['layer_distribution'][1] >= 1),
            ("Retrieval accuracy >= 80%", len([r for r in self.test_results['metrics']['retrieval'] if r['found']]) / len(self.test_results['metrics']['retrieval']) >= 0.80),
            ("Average coherence >= 0.85", avg_coherence >= 0.85)
        ]
        
        all_passed = True
        for test_name, passed in tests:
            status = "✅ PASS" if passed else "❌ FAIL"
            print(f"   {status}: {test_name}")
            if not passed:
                all_passed = False
        
        print("\n" + "="*60)
        if all_passed:
            print("✅ ALL TESTS PASSED")
        else:
            print("⚠️ SOME TESTS FAILED")
        print("="*60)
        
        self.test_results['assessment']['all_tests_passed'] = all_passed
        self.test_results['assessment']['individual_tests'] = [
            {'name': name, 'passed': passed} for name, passed in tests
        ]
    
    def calculate_max_depth(self):
        """Calculate maximum depth of realization graph"""
        def get_depth(r_id, visited=None):
            if visited is None:
                visited = set()
            if r_id in visited:
                return 0
            visited.add(r_id)
            
            r = self.engine.index.get(r_id)
            if not r or not r.children:
                return 1
            
            return 1 + max(get_depth(child_id, visited.copy()) for child_id in r.children)
        
        # Find root nodes (no parents)
        roots = [r.id for r in self.engine.index.values() if not r.parents]
        
        if not roots:
            return 0
        
        return max(get_depth(root) for root in roots)
    
    def export_results(self):
        """Export test results to JSON"""
        
        output_path = 'layers/layer_3_optimization/test_case_results.json'
        
        with open(output_path, 'w') as f:
            json.dump(self.test_results, f, indent=2)
        
        print(f"✅ Results exported to {output_path}")
        print(f"   Total size: {len(json.dumps(self.test_results))} bytes")
        
        # Also export the full engine state
        engine_state = self.engine.export_state()
        engine_path = 'layers/layer_3_optimization/test_case_engine_state.json'
        
        with open(engine_path, 'w') as f:
            json.dump(engine_state, f, indent=2)
        
        print(f"✅ Engine state exported to {engine_path}")


if __name__ == "__main__":
    # Run the comprehensive test
    test = TestCaseStudy()
    test.run_test()